Open AccessAutomatic Detection of Display Defects for Smart Meters based on Deep Learning
Author(s) -
Ye Chen,
Zhihu Hong,
Yaohua Liao,
Mengmeng Zhu,
Tong-Seok Han,
Qian Shen
Publication year - 2021
Publication title -
cit. journal of computing and information technology/journal of computing and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 27
eISSN - 1846-3908
pISSN - 1330-1136
DOI - 10.20532/cit.2020.1005158
Subject(s) - liquid crystal display , computer science , smart meter , artificial intelligence , fault (geology) , fault detection and isolation , deep learning , metre , computer hardware , embedded system , smart grid , computer vision , electrical engineering , engineering , physics , astronomy , seismology , geology , actuator , operating system
The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen.